Assisting people with daily living tasks in their own homes with a robot requires a navigation through a cluttered and varying environment. Sometimes the only possible path would be blocked by an obstacle which needs to be moved away but not into other obstructing regions like the space required for opening a door. This paper presents semantic assisted path planning in which a gridded semantic map is used to improve navigation among movable obstacles (NAMO) and partially plan simple household tasks like cleaning a carpet or moving objects to another location. Semantic planning allows the execution of tasks expressed in human-like form instead of mathematical concepts like coordinates. In our numerical experiments, spatial planning was completed well within a typical human-human dialogue response time, allowing for an immediate response by the robot.

This work explored the requirements of accurately and reliably predicting user intention using a deep learning methodology when performing fine-grained movements of the human hand. The focus was on combining a feature engineering process with the effective capability of deep learning to further identify salient characteristics from a biological input signal. 3 time domain features (root mean square, waveform length, and slope sign changes) were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements performed by 40 subjects. The feature data was mapped to 6 sensor bend resistance readings from a CyberGlove II system, representing the associated hand kinematic data. These sensors were located at specific joints of interest on the human hand (the thumb's metacarpophalangeal joint, the proximal interphalangeal joint of each finger, and the radiocarpal joint of the wrist). All datasets were taken from database 2 of the NinaPro online database repository. A 3-layer long short-term memory model with dropout was developed to predict the 6 glove sensor readings using a corresponding sEMG feature vector as input. Initial results from trials using test data from the 40 subjects produce an average mean squared error of 0.176. This indicates a viable pathway to follow for this prediction method of hand movement data, although further work is needed to optimize the model and to analyze the data with a more detailed set of metrics.

This paper presents an evidence-based overview of the functionality that robotic care systems should provide. The results identify a number of key characteristics that range from existing commercial products to research prototypes. For example, social care needs voice assistance that already exists in the form of smart speakers. Such systems provide an opportunity for entertainment and the ability to stay in contact with caregivers, friends and family. Consequently, a good speech recognition and ability to perform conversations were highly valued by elderly users. In contrast, care robots providing physical assistance still have not left the prototype phase and generally, do not have enough skills to be considered useful in the home. The results highlight the fact that the most common difficulties the elderly experience have not been solved and should be focused on in the future. The perception of usefulness and integration into the existing home of an elderly person are the main barriers to a robot being accepted as a part of the household. One of the paper's conclusions is that an ecosystem open to independent developers could greatly increase the robotic skillset and the chance that it can perform a useful task for its user.